Whether you’re talking about sport, chess or music, a surfeit of research has shown that the best performing experts practice more than their less able colleagues.

What’s unclear is whether the benefits of this practice are ongoing throughout a person’s career and secondly, whether the benefits of practice vary with a person’s level of skill. Are the most elite performers of such a high standard because of all the practice they do, or is it because of their superior talent that this practice is beneficial?

These questions are addressed in a new study of elite teenage chess players in the Netherlands, taking advantage of what’s known as linear mixed methods analysis to compare the effects of multiple factors over time, both within and between separate groups.

Anique de Bruin and colleagues were particularly interested in comparing the effects of deliberate, focused practice on those teenagers who remained in the Netherlands’ elite chess training programme, compared with the effects of practice on the performance of those who continued competing but who dropped out of the national training.

Forty-eight elite teenage players who stayed in the training scheme and thirty-three who dropped out answered questions about how many hours a week they spent practising. Their performance over the years was measured via their official chess ratings, collected between two to four times a year.

The headline result is that the benefits of practice are ongoing through the years – not just once a person has become elite – and that the players who dropped out performed less well, not because they benefited less from practice, but because they practised less. Assuming these findings translate to other domains of skill besides chess, these findings have implications for all of us.

The Digest caught up with co-author Niels Smits and asked him about the statistical approach taken in this study:

“Linear mixed models are a very elegant method of analyzing longitudinal data. They are very flexible for at least three reasons. First, in contrast to older methods such as repeated measures (M)ANOVA, they do not ask for complete data on all time points for all subjects. Consequently, one does not have to deal with missing data such as removing observations or imputing data points. Second, they do not ask for equal time intervals between the measurements; therefore subjects are allowed to differ in the moment of measurement. Time of measurement is simply entered as a covariate in the model to allow for a time effect. A third virtue, is that time varying covariates can be easily added to the model to determine how changes in these them influence the dependent variable.”

I don’t have access to the full article but… I smell multiple confounds. The groups were not randomized and, of course, then controlling statistically for confounds is fraught and imperfect at best. Perhaps those who dropped out lacked application, which also shows through during chess matches. (Not concentrating enough during the match, for example). I’m sure the authors tried to control for such confounds, but they almost certainly weren’t entirely successful.

I agree with the author’s assessments of the strengths of linear mixed models. Very useful, but can be tricky.Another great resource for learning about them is Lesa Hoffman’s website. Dr. Hoffman is a psychologist at U of Nebraska, who teaches graduate stats classes in linear mixed models. She puts her lectures and slides for free download on her site.http://psych.unl.edu/hoffman/HomePage.htm